Novel dna hypermethylation diagnostic biomarkers for colorectal cancer

ABSTRACT

The present invention relates to the field of cancer. More specifically, the present invention relates to the use of biomarkers to detect colorectal cancer. In one aspect, the present invention provides methods for qualifying colorectal cancer status including, but not limited to, diagnosis, prognosis, and risk stratification, in patients. In one embodiment, a method for diagnosing colorectal cancer (CRC) in a patient comprises the steps of (a) collecting a sample from the patient; (b) measuring the methylation levels of one or more biomarkers in the sample collected from the patient; and (c) comparing the methylation levels of the one or more biomarkers with predefined methylation levels of the same biomarkers that correlate to a patient having CRC and predefined methylation levels of the same biomarkers that correlate to a patient not having CRC, wherein a correlation to one of the predefined methylation levels provides the diagnosis.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/443,562, filed Feb. 16, 2011, and U.S. Provisional Application No.61/389,304, filed Oct. 4, 2010; both of which are incorporated herein byreference in their entireties.

STATEMENT OF GOVERNMENTAL INTEREST

This invention was made with U.S. government support under grant no.U01CA084986 and grant no. R01CA0133012. The U.S. government has certainrights in the invention.

FIELD OF THE INVENTION

The present invention relates to the field of cancer. More specifically,the present invention relates to the use of biomarkers to detectcolorectal cancer.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ELECTRONICALLY

This application contains a sequence listing. It has been submittedelectronically via EFS-Web as an ASCII text file entitled“P11173-02_Sequence_Listing_ST25.txt.” The sequence listing is 36,807bytes in size, and was created on Oct. 3, 2011. It is herebyincorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

In the United States, colorectal cancer (CRC) is the third mostprevalent and the second most deadly cancer in both sexes. Jemal et al.,58 CANCER J. FOR CLINICIANS 71-96 (2008). CRC is highly curable in itsearly, localized stages, with a 5-year survival rate exceeding 90%. Id.Unfortunately, 61% of new cases are already advanced at the time ofdiagnosis. Id. Delayed diagnosis occurs due to the asymptomatic natureof most early-stage CRCs; thus, the key to reducing deaths from CRC isperiodic screening of the entire colon in the average risk population.Kahi et al., 135 GASTROENTEROLOGY 380-99 (2008). The current goldstandard method for screening is colonoscopy. Id. However, invasivescreening modalities, including colonoscopy, are not ideal forapplication to the asymptomatic population. Therefore, activeinvestigations are now underway to discover noninvasive biomarkers, suchas those found in stool, which could supplement or supplant colonoscopicscreening.

Hypermethylation of CpG islands (CGIs) is a promising CRC biomarker withhigh potential for translation into non-invasive CRC detectionmodalities. CGI hypermethylation is a common epigenetic DNA abnormalitythat has been strongly linked to CRC. Fraga et al., 23 TRENDS IN GEN.413-18 (2007). CGI hypermethylation possesses several advantages as abiomarker: 1) hypermethylation at multiple CGIs often exists inadenomas, suggesting its potential utility in early detection (Kim etal., 45 GENES, CHROMOSOMES & CANCER 781-89 (2006)); 2) only one assayper locus is generally needed, in contrast to gene mutation, whichfrequently require multiple assays due to the presence of mutationalhotspots; and 3) quantitative methylation assays are applicable tolow-integrity DNA commonly encountered in clinical specimens (Uhlmann etal., 23 ELECTROPHORESIS 4072-79 (2002); Fads et al., 28 NUCL. ACIDS RES.E32 (2000)). However, known cancer-specific methylation targets in thecolon have in the past been identified based on their functionalrelevance to neoplastic progression, rather than on their merit asbiomarkers, partly due to the previous lack of genome-wide,high-resolution methodologies for the direct analysis of methylation.

Recent technological advances now offer the ability to performhigh-throughput, direct assays of DNA methylation. See Estecio et al.,17 GENOME RES. 1529-36 (2007). In this study, loyed a microarray-baseddirect scanning assay of DNA methylation to extensively search for CGIhypennethylation events, based purely on their performance as CRCbiomarkers, for ultimate application to the average-risk population.

SUMMARY OF THE INVENTION

The present invention is based, at least in part, on the discovery oftwelve DNA regions at which abnormal methylation occurs uniquely andprevalently in colorectal neoplasias. Real-time quantitativemethylation-specific polymerase chain reaction (PCR) analysis of theseDNA regions revealed that all of these methylation markers, asindividual markers and multi-locus panel markers, can distinguishcolorectal neoplasias from colonic mucosae of colonic neoplasia-freecontrol cases with high accuracy. Abnormal methylation of these DNAregions was similarly prevalent in advanced adenomas, local colorectalcarcinomas, and metastatic colorectal carcinomas, indicating the utilityof these methylation markers for the detection of a wide range ofdiseases.

Accordingly, in one aspect, the present invention provides methods forqualifying colorectal cancer status including, but not limited to,diagnosis, prognosis, and risk stratification, in patients. In oneembodiment, a method for diagnosing colorectal cancer (CRC) in a patientcomprises the steps of (a) collecting a sample from the patient; (b)measuring the methylation levels of one or more biomarkers in the samplecollected from the patient; and (c) comparing the methylation levels ofthe one or more biomarkers with predefined methylation levels of thesame biomarkers that correlate to a patient having CRC and predefinedmethylation levels of the same biomarkers that correlate to a patientnot having CRC, wherein a correlation to one of the predefinedmethylation levels provides the diagnosis.

In particular embodiments, the one or more biomarkers is selected fromthe group consisting of VSX2, NPTX1, BEND4, ALX3, miR34b, BTG4, GLP1R,HOMER2, GJC1, DOCK8, ZNF583, and NME4. Any of the foregoing biomarkersbe used individually or in combination with one another or other knownbiomarkers to qualify disease status as described herein. In a specificembodiment, the one or more biomarkers comprise ALX3, miR34b or both. Inanother embodiment, the one or more biomarkers comprises VSX2. In yetanother embodiment, the one or more biomarkers comprise VSX2, NPTX1,BEND4, miR34b, and HOMER2. In a further embodiment, the one or morebiomarkers comprise VSX2, BEND4, GLP1R, HOMER2, GJC1, ZNF583. In such anembodiment, the one or more biomarkers further comprises NME4.

In another embodiments of the present invention, a method for diagnosingcolorectal cancer (CRC) in a patient comprises the steps of (a)collecting a sample from the patient; (b) measuring the methylationlevels of a panel of biomarkers in the sample collected from thepatient, wherein the panel of biomarkers comprises VSX2, NPTX1, BEND4,ALX3, miR34b, BTG4, GLP1R, HOMER2, GJC1, DOCK8, ZNF583, and NME4; and(c) comparing the methylation levels of the panel of biomarkers withpredefined methylation levels of the same panel of biomarkers thatcorrelate to a patient having CRC and predefined methylation levels ofthe same panel of biomarkers that correlate to a patient not having CRC,wherein a correlation to one of the predefined methylation levelsprovides the diagnosis.

In an alternative embodiment, a method for diagnosing colorectal cancer(CRC) in a patient comprises the steps of (a) collecting a sample fromthe patient; (b) measuring the methylation levels of a panel ofbiomarkers in the sample collected from the patient, wherein the panelof biomarkers comprises VSX2 and ALX3; and (c) comparing the methylationlevels of the panel of biomarkers with predefined methylation levels ofthe same biomarkers that correlate to a patient having CRC andpredefined methylation levels of the same biomarkers that correlate to apatient not having CRC, wherein a correlation to one of the predefinedmethylation levels provides the diagnosis. In a specific embodiment, thepanel of biomarkers further comprises miR34b. In another embodiment, thepanel of biomarkers further comprises miR34b, NPTX1, BEND4, BTG4, GLP1R,HOMER2, GJC1, DOCK8, ZNF583, and NME4.

In a more specific embodiment, a method for diagnosing colorectal cancer(CRC) in a patient comprises the steps of (a) collecting a stool samplefrom the patient; (b) measuring the methylation levels of a panel ofbiomarkers in the stool sample collected from the patient, wherein thepanel of biomarkers comprises ALX3 and miR34b; and (c) comparing themethylation levels of the panel of biomarkers with predefinedmethylation levels of the same biomarkers that correlate to a patienthaving CRC and predefined methylation levels of the same biomarkers thatcorrelate to a patient not having CRC, wherein a correlation to one ofthe predefined methylation levels provides the diagnosis. In a furtherembodiment, the panel of biomarkers further comprises VSX2. In yetanother embodiment, the panel of biomarkers further comprises VSX2,NPTX1, BEND4, BTG4, GLP1R, HOMER2, GJC1, DOCK8, ZNF583, and NME4.

In a specific embodiment, a method for determining the CRC status in apatient comprises the steps of (a) collecting a sample from the patient;(b) measuring the methylation levels of a panel of biomarkers in thesample collected from the patient, wherein the panel of biomarkerscomprises VSX2, NPTX1, BEND4, ALX3, miR34b, BTG4, GLP1R, HOMER2, GJC1,DOCK8, ZNF583, and NME4; and (c) comparing the methylation levels of thepanel of biomarkers with predefined methylation levels of the same panelof biomarkers that correlate to one or more CRC statuses selected fromthe group consisting of having CRC, not having CRC, progressing CRC, andregressing CRC, wherein a correlation to one of the predefinedmethylation levels determines the CRC status of the patient.

In certain embodiments of the present invention, the measuring step cancomprise restriction enzyme digestion of the sample followed byreal-time quantitative methylation-specific polymerase chain reaction.Further, the sample can be any suitable biological sample including, butnot limited to, a stool, blood or serum sample. In a specificembodiment, the sample is a stool sample. In another embodiment, thesample is a serum sample.

In another aspect, the present invention provides kits useful fordetermining CRC status in a patient. In certain embodiments, a kitcomprises (a) a substrate for collecting a biological sample from thepatient; and (b) a means for measuring the methylation levels of one ormore biomarkers selected from the group consisting of VSX2, NPTX 1,BEND4, ALX3, miR34b, BTG4, GLP1R, HOMER2, GJC1, DOCK8, ZNF583, and NME4.In particular embodiments, the means for measuring the methylationlevels of one or more biomarkers are oligonucleotide primers specificfor amplifying methylated regions of the biomarkers.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is an outline of the experimental strategy and results.

FIG. 2 shows loci percent methylation ratio (PMR) for neoplastic andnon-neoplastic colonic tissues. The box plots represent the quantitativemethylation-specific PCR (qMSP) results of 51 colorectal cancers (CRCs)and nine adenomas, and 53 non-neoplastic colonic mucosal tissues (NCs,eight young control NCs, 26 control NCs, and 19 CRC-NCs). The Y-axisrepresents PMR value. Data on 11 loci that demonstrated methylation inat least one of the neoplastic tissues are shown. Median (bar), 25-75percentile range (box), and 10-90 percentile range (whisker) of allinformative specimens are displayed for each tissue category. Single-,double-, and triple asterisks indicate significant difference fromcontrol NCs at P level <0.05, <0.01, and <1×10⁻⁶, respectively.

FIG. 3 presents the receiver operating characteristic (ROC) curve-basedassessment of the methylation markers' diagnostic accuracy. In FIG. 3A,ROC curves representing the distinction of CRCs from control NC areshown for the seven loci demonstrating area under ROC (AUROC)values >0.8. Mean and 95% confidence interval (CI) of AUROC as well as Pvalue are shown in each panel. In FIG. 3B, ROC curves based onmulti-loci diagnostic panels are shown for the distinction of CRCs fromcontrol NCs. Gray solid line and black solid line correspond to theprofiles for best single locus (ALX3) and the multilocus panels (ALX3,ZNF583, miR-34b, and VSX2), respectively.

FIG. 4 is a table summarizing demographic data for cases studied inmicroarray/methylation-specific PCR (MSP) experiments and real-timequantitative MSP (qMSP) experiments.

FIG. 5 is a table showing ROC curve analysis data for the discriminationfrom control NCs.

FIG. 6 is a table showing the primer sequences used in themethylation-specific polymerase chain reaction (PCR) analyses.

FIG. 7 depicts the MCAM-derived (Methylated CpG island amplificationcoupled with microarray) methylation profiles of previously reported CRCmethylation markers.

FIG. 8 shows validation results of twenty candidate loci forcancer-specific hypermethylation.

FIG. 9 demonstrates MCAM data reproducibility and reliability. In FIG.9A, the raw data for two experimental batches of a specimen demonstratedextremely high correlation (R>0.99). Each data point represents a probe.The MCAM experiment for these two batches (i.e., DNA processing, arrayhybridization, and array scanning) was performed on separate days, twoweeks apart. (B) Methylation measurements by MCAM (X-axis) and qMSP(Y-ax is) are plotted for individual specimens at four loci. The resultsfrom MCAM and qMSP assays at these loci correlated well (R>0.70,p<0.0001) despite of the markedly distinctive basis of methylationmeasurement for MCAM vs. qMSP (e.g., restriction enzyme digestion vs.bisulfite conversion, single CpG methylation status vs. continuousmethylation of all or nearly all CpGs within a region that is 70-120bases in length).

FIG. 10 presents the cluster analysis of the methylation microarraydata. The k-mean Clustergram is shown for the analysis of 18,892autosomal loci that tended to be differentially methylated between 17CRCs and 8 control NCs (inclusion criteria: t-test p<0.1). The y-axisrepresents loci alignment, while the x-axis represents tissue alignment:orange, control NCs; pink, CIMP(+) CRCs (CpG island methylatorphenotype, CIMP); blue, CIMP(−) CRCs. As expected, control NCs clusteredseparately from CRCs, and CIMP(−) CRCs formed a cluster separately frommost CIMP(−) CRCs. Gray vertical bars indicate the clusters of lociwhose methylation status in CIMP(+) CRCs differs from that of CIMP(−)CRCs.

FIG. 11 shows the ALX3 methylation status for non-neoplastic colonictissues from CRC cases as well as neoplasia-free control cases. Median(bar), 25-75 percentile range (box), and 10-90 percentile range(whisker) of all informative specimens are displayed for each tissuecategory. P-values were calculated by Mann-Whitney test.

FIG. 12 depicts the performance of the stool methylation biomarker ALX3.FIG. 12A shows the bisulfate pyrosequencing results of ALX3 in stoolsamples from CRC and CRA patients, and subjects without colonic lesions(NC). FIG. 12B shows the ROC (receiver-operating characteristics) curvesfor ALX3 in subjects with colorectal neoplasia (CRA and CRC) vs.subjects without any colonic lesions.

FIG. 13 shows the methylation of ALX3 and miR-34b in serum samples. ThePMR (percentage or methylated reference) measured by MethyLight isplotted for serum samples from 10 healthy individuals and 9 CRCpatients. Methylation was undetectable in all healthy control subjects'serums for both loci.

FIG. 13 shows the ALX gene sequence. ALX3 exon 1: Italics andunderlined(reverse direction). ALX3 qMSP amplicon: Boxed. Bold font: theregion whose cancer-associated methylation is verified in the currentstudy using methylation microarray, bisulfite sequencing, or qMSP.

FIG. 14 shows the VSX2 gene sequence. VSX2 exon 1 and exon 2: italicsand underlined. VSX2 qMSP amplicon: boxed. Bold font: the region whosecancer-associated methylation is verified in the current study usingmethylation microarray, bisulfite sequencing, or qMSP.

FIG. 15 shows the NPTX1 gene sequence. NPTX1 exon 1 and 2: italics andunderlined (reverse direction). NPTX1 qMSP amplicon: boxed. Bold font:the region whose cancer-associated methylation is verified in thecurrent study using methylation microarray, bisulfite sequencing, orqMSP.

FIG. 16 shows the BEND4 sequence. BEND4 exon 1 and exon 2: italics andunderlined(reverse direction). BEND4 qMSP amplicon: boxed. Bold font:the region whose cancer-associated methylation is verified in thecurrent study using methylation microarray, bisulfite sequencing, orqMSP.

FIG. 17 shows the miR34b and BTG4 gene sequences. miR34b exon: boxed andgray highlight (forward direction). BTG4 exon 1: italics and underlined(reverse direction). BTG4 qMSP amplicon: boxed. miR34b qMSP amplicon:-Gray highlight. Bold font: the region whose cancer-associatedmethylation is verified in the current study using methylationmicroarray, bisulfite sequencing, or qMSP. Nucleotide numbering isaccording to the UCSC hg18.

FIG. 18 shows the GLP1R gene sequences. GLP1R exon 1: italics andunderlined. GLP1R qMSP amplicon: boxed. Bold font: the region whosecancer-associated methylation is verified in the current study usingmethylation microarray, bisulfite sequencing, or qMSP.

FIG. 19 shows the HOMER2 gene sequences. HOMER2 exon 1: italics andunderlined (reverse direction). HOMER2 qMSP amplicon: boxed. Bold font:the region whose cancer-associated methylation is verified in thecurrent study using methylation microarray, bisulfite sequencing, orqMSP.

FIG. 20 shows the GJC1 gene sequence. GJC1 exon 1: italics andunderlined (reverse direction). GJC1 qMSP amplicon: boxed. Bold font:the region whose cancer-associated methylation is verified in thecurrent study using methylation microarray, bisulfite sequencing, orqMSP.

FIG. 21 shows the DOCK8 gene sequence. DOCK8 exon 1: italics andunderlined. DOCK8 qMSP amplicon: boxed. Bold font: the region whosecancer-associated methylation is verified in the current study usingmethylation microarray, bisulfite sequencing, or qMSP.

FIG. 22 shows the ZNF583 gene sequence. ZNF583 exon 1: italics andunderlined. ZNF583 qMSP amplicon: boxed. Bold font: the region whosecancer-associated methylation is verified in the current study usingmethylation microarray, bisulfite sequencing, or qMSP.

FIG. 23 shows the NME4 gene sequence. NME4 exon 1: italics andunderlined. NME4 qMSP amplicon: boxed. Bold font: the region whosecancer-associated methylation is verified in the current study usingmethylation microarray, bisulfite sequencing, or qMSP.

DETAILED DESCRIPTION OF THE INVENTION

It is understood that the present invention is not limited to theparticular methods and components, etc., described herein, as these mayvary. It is also to be understood that the terminology used herein isused for the purpose of describing particular embodiments only, and isnot intended to limit the scope of the present invention. It must benoted that as used herein and in the appended claims, the singular forms“a,” “an,” and “the” include the plural reference unless the contextclearly dictates otherwise. Thus, for example, a reference to a“protein” is a reference to one or more proteins, and includesequivalents thereof known to those skilled in the art and so forth.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly-understood by one of ordinary skill inthe art to which this invention belongs. Specific methods, devices, andmaterials are described, although any methods and materials similar orequivalent to those described herein can be used in the practice ortesting of the present invention.

All publications cited herein are hereby incorporated by referenceincluding all journal articles, books, manuals, published patentapplications, and issued patents. In addition, the meaning of certainterms and phrases employed in the specification, examples, and appendedclaims are provided. The definitions are not meant to be limiting innature and serve to provide a clearer understanding of certain aspectsof the present invention.

I. Definitions

As used herein, the term “comparing” refers to making an assessment ofhow the proportion, level or cellular localization of one or morebiomarkers in a sample from a patient relates to the proportion, levelor cellular localization of the corresponding one or more biomarkers ina standard or control sample. For example, “comparing” may refer toassessing whether the proportion, level, or cellular localization of oneor more biomarkers in a sample from a patient is the same as, more orless than, or different from the proportion, level, or cellularlocalization of the corresponding one or more biomarkers in standard orcontrol sample. More specifically, the term may refer to assessingwhether the proportion, level, or cellular localization of one or morebiomarkers in a sample from a patient is the same as, more or less than,different from or otherwise corresponds (or not) to the proportion,level, or cellular localization of predefined biomarker levels thatcorrespond to, for example, a patient having CRC, not having CRC, isresponding to treatment for CRC, is not responding to treatment for CRC,is/is not likely to respond to a particular CRC treatment, or having/nothaving another disease or condition. In a specific embodiment, the term“comparing” refers to assessing whether the methylation level of one ormore biomarkers of the present invention in a sample from a patient isthe same as, more or less than, different from other otherwisecorrespond (or not) to methylation levels of the same biomarkers in acontrol sample (e.g., predefined levels that correlate to uninfectedindividuals, standard CRC levels, etc.).

As used herein, the terms “indicates” or “correlates” (or “indicating”or “correlating,” or “indication” or “correlation,” depending on thecontext) in reference to a parameter, e.g., a modulated proportion,level, or cellular localization in a sample from a patient, may meanthat the patient has CRC. In specific embodiments, the parameter maycomprise the methylation status or level of one or more biomarkers ofthe present invention. A particular set or pattern of methylation of oneor more biomarkers may indicate that a patient has CRC (i.e., correlatesto a patient having CRC). In other embodiments, a particular set orpattern of methylation of one or more biomarkers may be correlated to apatient being unaffected. In certain embodiments, “indicating,” or“correlating,” as used according to the present invention, may be by anylinear or non-linear method of quantifying the relationship betweenmethylation levels of biomarkers to a standard, control or comparativevalue for the assessment of the diagnosis, prediction of CRC or CRCprogression, assessment of efficacy of clinical treatment,identification of a patient that may respond to a particular treatmentregime or pharmaceutical agent, monitoring of the progress of treatment,and in the context of a screening assay, for the identification of ananti-CRC therapeutic.

The terms “patient,” “individual,” or “subject” are used interchangeablyherein, and refer to a mammal, particularly, a human. The patient mayhave mild, intermediate or severe disease. The patient may be treatmentnaïve, responding to any form of treatment, or refractory. The patientmay be an individual in need of treatment or in need of diagnosis basedon particular symptoms or family history. In some cases, the terms mayrefer to treatment in experimental animals, in veterinary application,and in the development of animal models for disease, including, but notlimited to, rodents including mice, rats, and hamsters; and primates.

The terms “measuring” and “determining” are used interchangeablythroughout, and refer to methods which include obtaining a patientsample and/or detecting the methylation status or level of abiomarker(s) in a sample. In one embodiment, the terms refer toobtaining a patient sample and detecting the methylation status or levelof one or more biomarkers in the sample. In another embodiment, theterms “measuring” and “determining” mean detecting the methylationstatus or level of one or more biomarkers in a patient sample. Measuringcan be accomplished by methods known in the art and those furtherdescribed herein including, but not limited to, quantitative polymerasechain reaction (PCR). The term “measuring” is also used interchangeablythroughout with the term “detecting.”

The term “methylation” refers to cytosine methylation at positions C5 orN4 of cytosine, the N6 position of adenine or other types of nucleicacid methylation. In vitro amplified DNA is unmethylated because invitro DNA amplification methods do not retain the methylation pattern ofthe amplification template. However, “unmethylated DNA” or “methylatedDNA” can also refer to amplified DNA whose original template wasunmethylated or methylated, respectively. By “hypermethylation” or“elevated level of methylation” is meant an increase in methylation of aregion of DNA (e.g., a biomarker of the present invention) that isconsidered statistically significant over levels of a controlpopulation. “Hypermethylation” or “elevated level of methylation” mayrefer to increased levels seen in a patient over time.

In particular embodiments, a biomarker would be unmethylated in a normalsample (e.g., normal or control tissue without disease, or normal orcontrol body fluid, stool, blood, serum), most importantly in thehealthy tissue the tumor originates from and/or in healthy stool, blood,serum, or other body fluid. In other embodiments, a biomarker would behypermethylated in a large fraction of the tumors, preferably at amethylation frequency of at least about 50%, at least about 60%, atleast about 70%, at least about 75%, at least about 80%, at least about85%, at least about 90%, at least about 95%, or about 100%. Inparticular embodiment, the methylation status/levels of the biomarkerscan be used to differentiate between different subtypes or tumorentities. Specific DNA methylation patterns may distinguish tumors withlow and high metastatic potential making it possible to apply optimaltreatment regimens early. In additional, methylation of certain DNArepair or damage response genes may be predictive of a positivetherapeutic response.

A “methylation profile” refers to a set of data representing themethylation states or levels of one or more loci within a molecule ofDNA from e.g., the genome of an individual or cells or sample from anindividual. The profile can indicate the methylation state of every basein an individual, can comprise information regarding a subset of thebase pairs (e.g., the methylation state of specific restriction enzymerecognition sequence) in a genome, or can comprise information regardingregional methylation density of each locus. In some embodiments, amethylation profile refers to the methylation states or levels of one ormore biomarkers described herein, including VSX2, NPTX1, BEND4, ALX3,miR34b, BTG4, GLP1R, HOMER2, GJC 1, DOCK8, ZNF583, and NME4.

The terms “methylation status” or “methylation level” refers to thepresence, absence and/or quantity of methylation at a particularnucleotide, or nucleotides within a portion of DNA. The methylationstatus of a particular DNA sequence (e.g., a DNA biomarker or DNA regionas described herein) can indicate the methylation state of every base inthe sequence or can indicate the methylation state of a subset of thebase pairs (e.g., of cytosines or the methylation state of one or morespecific restriction enzyme recognition sequences) within the sequence,or can indicate information regarding regional methylation densitywithin the sequence without providing precise information of where inthe sequence the methylation occurs. The methylation status canoptionally be represented or indicated by a “methylation value” or“methylation level.” A methylation value or level can be generated, forexample, by quantifying the amount of intact DNA present followingrestriction digestion with a methylation dependent restriction enzyme.In this example, if a particular sequence in the DNA is quantified usingquantitative PCR, an amount of template DNA approximately equal to amock treated control indicates the sequence is not highly methylatedwhereas an amount of template substantially less than occurs in the mocktreated sample indicates the presence of methylated DNA at the sequence.Accordingly, a value, i.e., a methylation value, for example from theabove described example, represents the methylation status and can thusbe used as a quantitative indicator of methylation status. This is ofparticular use when it is desirable to compare the methylation status ofa sequence in a sample to a threshold value.

A “methylation-dependent restriction enzyme” refers to a restrictionenzyme that cleaves or digests DNA at or in proximity to a methylatedrecognition sequence, but does not cleave DNA at or near the samesequence when the recognition sequence is not methylated.Methylation-dependent restriction enzymes include those that cut at amethylated recognition sequence (e.g., Dpn1) and enzymes that cut at asequence near but not at the recognition sequence (e.g., McrBC). Forexample, McrBC's recognition sequence is 5′ RmC (N40-3000) RmC 3′ where“R” is a purine and “mC” is a methylated cytosine and “N40-3000”indicates the distance between the two RmC half sites for which arestriction event has been observed. McrBC generally cuts close to onehalf-site or the other, but cleavage positions are typically distributedover several base pairs, approximately 30 base pairs from the methylatedbase. McrBC sometimes cuts 3′ of both half sites, sometimes 5′ of bothhalf sites, and sometimes between the two sites. Exemplarymethylation-dependent restriction enzymes include, e.g., McrBC, McrA,MrrA, BisI, GlaI and DpnI. One of skill in the art will appreciate thatany methylation-dependent restriction enzyme, including homologs andorthologs of the restriction enzymes described herein, is also suitablefor use in the present invention.

A “methylation-sensitive restriction enzyme” refers to a restrictionenzyme that cleaves DNA at or in proximity to an unmethylatedrecognition sequence but does not cleave at or in proximity to the samesequence when the recognition sequence is methylated. Exemplarymethylation-sensitive restriction enzymes are described in, e.g.,McClelland et al., 22(17) NUCLEIC ACIDS RES. 3640-59 (1994) andhttp://rebase.neb.com. Suitable methylation-sensitive restrictionenzymes that do not cleave DNA at or near their recognition sequencewhen a cytosine within the recognition sequence is methylated atposition C⁵ include, e.g., Mt II, Aci I, Acd I, Age I, Alu I, Ase I, AseI, AsiS I, Bbe I, BsaA I, BsaH I, BsiE I, BsiW I, BsrF I, BssH II, BssKI, BstB I, BstN I, BstU I, Cla I, Eae I, Eag I, Fau I, Fse I, Hha I,HinP1 I, HinC II, Hpa II, Hpy99 I, HpyCH4 IV, Kas I, Mbo I, Mlu I, MapAlI, Msp I, Nae I, Nar I, Not I, Pml I, Pst I, Pvu I, Rsr II, Sac II, SapI, Sau3A I, Sfl I, Sfo I, SgrA I, Sma I, SnaB I, Tsc I, Xma I, and ZraI. Suitable methylation-sensitive restriction enzymes that do not cleaveDNA at or near their recognition sequence when an adenosine within therecognition sequence is methylated at position N⁶ include, e.g., Mbo I.One of skill in the art will appreciate that any methylation-sensitiverestriction enzyme, including homologs and orthologs of the restrictionenzymes described herein, is also suitable for use in the presentinvention. One of skill in the art will further appreciate that amethylation-sensitive restriction enzyme that fails to cut in thepresence of methylation of a cytosine at or near its recognitionsequence may be insensitive to the presence of methylation of anadenosine at or near its recognition sequence. Likewise, amethylation-sensitive restriction enzyme that fails to cut in thepresence of methylation of an adenosine at or near its recognitionsequence may be insensitive to the presence of methylation of a cytosineat or near its recognition sequence. For example, Sau3AI is sensitive(i.e., fails to cut) to the presence of a methylated cytosine at or nearits recognition sequence, but is insensitive (i.e., cuts) to thepresence of a methylated adenosine at or near its recognition sequence.One of skill in the art will also appreciate that somemethylation-sensitive restriction enzymes are blocked by methylation ofbases on one or both strands of DNA encompassing of their recognitionsequence, while other methylation-sensitive restriction enzymes areblocked only by methylation on both strands, but can cut if arecognition site is hemi-methylated.

The terms “sample,” “patient sample,” “biological sample,” and the like,encompass a variety of sample types obtained from a patient, individual,or subject and can be used in a diagnostic or monitoring assay. Thepatient sample may be obtained from a healthy subject, a diseasedpatient or a patient having associated symptoms of CRC. Moreover, asample obtained from a patient can be divided and only a portion may beused for diagnosis. Further, the sample, or a portion thereof, can bestored under conditions to maintain sample for later analysis. Thedefinition specifically encompasses blood and other liquid samples ofbiological origin (including, but not limited to, peripheral blood,serum, plasma, urine, saliva, stool and synovial fluid), solid tissuesamples such as a biopsy specimen or tissue cultures or cells derivedtherefrom and the progeny thereof. In a specific embodiment, a samplecomprises a blood sample. In another embodiment, a serum sample is used.In another embodiment, a sample comprises a stool sample. The definitionalso includes samples that have been manipulated in any way after theirprocurement, such as by centrifugation, filtration, precipitation,dialysis, chromatography, treatment with reagents, washed, or enrichedfor certain cell populations. The terms further encompass a clinicalsample, and also include cells in culture, cell supernatants, tissuesamples, organs, and the like. Samples may also comprise fresh-frozenand/or formalin-fixed, paraffin-embedded tissue blocks, such as blocksprepared from clinical or pathological biopsies, prepared forpathological analysis or study by immunohistochemistry.

Various methodologies of the instant invention include a step thatinvolves comparing a value, level, feature, characteristic, property,etc. to a “suitable control,” referred to interchangeably herein as an“appropriate control” or a “control sample.” A “suitable control,”“appropriate control” or a “control sample” is any control or standardfamiliar to one of ordinary skill in the art useful for comparisonpurposes. In one embodiment, a “suitable control” or “appropriatecontrol” is a value, level, feature, characteristic, property, etc.,determined in a cell, organ, or patient, e.g., a control or normal cell,organ, or patient, exhibiting, for example, normal traits. For example,the biomarkers of the present invention may be assayed for theirmethylation level in a sample from an unaffected individual (UI) or anormal control individual (NC) (both terms are used interchangeablyherein). In another embodiment, a “suitable control” or “appropriatecontrol” is a value, level, feature, characteristic, property, etc.determined prior to performing a therapy (e.g., a CRC treatment) on apatient. In yet another embodiment, a transcription rate, mRNA level,translation rate, protein level, biological activity, cellularcharacteristic or property, genotype, phenotype, etc. can be determinedprior to, during, or after administering a therapy into a cell, organ,or patient. In a further embodiment, a “suitable control” or“appropriate control” is a predefined value, level, feature,characteristic, property, etc. A “suitable control” can be a methylationprofile of one or more biomarkers of the present invention thatcorrelates to CRC, to which a patient sample can be compared. Thepatient sample can also be compared to a negative control, i.e., amethylation profile that correlates to not having CRC.

II. Hypermethylated Biomarkers and Detection Thereof

The biomarkers of the present invention are differentially methylated inCRC versus normal tissue. Such biomarkers can be used individually asdiagnostic tool, or in combination as a biomarker panel. In particularembodiments, the biomarkers include VSX2, NPTX1, BEND4, ALX3, miR34b,BTG4, GLP1R, HOMER2, GJC1, DOCK8, ZNF583, and NME4. In fact, anycombination of the biomarkers can be used as a diagnostic tool. Thesequences of these biomarkers are publicly available, specifically, VSX2(Gene Id No. 338917), NPTX1 (Gene Id No. 4884), BEND4 (Gene Id No.389206), ALX3 (Gene Id No. 257), miR34b (Gene Id No. 407041), BTG4 (GeneId No. 54766), GLP1R (Gene Id No. 2740), HOMER2 (Gene Id No. 9455), GJC1(Gene Id No. 10052), DOCKS (Gene Id No. 81704), ZNF583 (Gene Id No.27033), and NME4 (Gene Id No. 4833).

The DNA biomarkers of the present invention comprise fragments of apolynucleotide (e.g., regions of genome polynucleotide or DNA) whichlikely contain CpG island(s), or fragments which are more susceptible tomethylation or demethylation than other regions of genome DNA. The term“CpG islands” is a region of genome DNA which shows higher frequency of5′-CG-3′ (CpG) dinucleotides than other regions of genome DNA.Methylation of DNA at CpG dinucleotides, in particular, the addition ofa methyl group to position 5 of the cytosine ring at CpG dinucleotides,is one of the epigenetic modifications in mammalian cells. CpG islandsoften harbor the promoters of genes and play a pivotal role in thecontrol of gene expression. In normal tissues CpG islands are usuallyunmethylated, but a subset of islands becomes methylated during thedevelopment of a disease (e.g., tumor development). Changes in DNAmethylation patterns can occur in a developmental stage and tissuespecific manner and often accompany tumor development, most notably inthe form of CpG island hypermethylation. During tumorigenesis, bothalleles of a tumor suppressor gene need to be inactivated by genomicchanges such as chromosomal deletions or loss-of-function mutations inthe coding region of a gene. As an alternative mechanism,transcriptional silencing by hypermethylation of CpG islands spanningthe promoter regions of tumor suppressor genes is a common and importantprocess in carcinogenesis. Since hypermethylation generally leads toinactivation of gene expression, this epigenetic alteration isconsidered to be a key mechanism for long-term silencing of tumorsuppressor genes.

There are a number of methods that can be employed to measure, detect,determine, identify, and characterize the methylation status/level of abiomarker (i.e., a region/fragment of DNA or a region/fragment of genomeDNA (e.g., CpG island-containing region/fragment)) in the development ofa disease (e.g., colorectal cancer) and thus diagnose the onset,presence or status of the disease.

In some embodiments, methods for detecting methylation include randomlyshearing or randomly fragmenting the genomic DNA, cutting the DNA with amethylation-dependent or methylation-sensitive restriction enzyme andsubsequently selectively identifying and/or analyzing the cut or uncutDNA. Selective identification can include, for example, separating cutand uncut DNA (e.g., by size) and quantifying a sequence of interestthat was cut or, alternatively, that was not cut. See, e.g., U.S. Pat.No. 7,186,512. Alternatively, the method can encompass amplifying intactDNA after restriction enzyme digestion, thereby only amplifying DNA thatwas not cleaved by the restriction enzyme in the area amplified. See,e.g., U.S. Pat. No. 7,910,296; No. 7,901,880; and No. 7,459,274. In someembodiments, amplification can be performed using primers that are genespecific. Alternatively, adaptors can be added to the ends of therandomly fragmented DNA, the DNA can be digested with amethylation-dependent or methylation-sensitive restriction enzyme,intact DNA can be amplified using primers that hybridize to the adaptorsequences. In this case, a second step can be performed to determine thepresence, absence or quantity of a particular gene in an amplified poolof DNA. In some embodiments, the DNA is amplified using real-time,quantitative PCR.

In other embodiments, the methods comprise quantifying the averagemethylation density in a target sequence within a population of genomicDNA. In some embodiments, the method comprises contacting genomic DNAwith a methylation-dependent restriction enzyme or methylation-sensitiverestriction enzyme under conditions that allow for at least some copiesof potential restriction enzyme cleavage sites in the locus to remainuncleaved; quantifying intact copies of the locus; and comparing thequantity of amplified product to a control value representing thequantity of methylation of control DNA, thereby quantifying the averagemethylation density in the locus compared to the methylation density ofthe control DNA.

The quantity of methylation of a locus of DNA can be determined byproviding a sample of genomic DNA comprising the locus, cleaving the DNAwith a restriction enzyme that is either methylation-sensitive ormethylation-dependent, and then quantifying the amount of intact DNA orquantifying the amount of cut DNA at the DNA locus of interest. Theamount of intact or cut DNA will depend on the initial amount of genomicDNA containing the locus, the amount of methylation in the locus, andthe number (i.e., the fraction) of nucleotides in the locus that aremethylated in the genomic DNA. The amount of methylation in a DNA locuscan be determined by comparing the quantity of intact DNA or cut DNA toa control value representing the quantity of intact DNA or cut DNA in asimilarly-treated DNA sample. The control value can represent a known orpredicted number of methylated nucleotides. Alternatively, the'controlvalue can represent the quantity of intact or cut DNA from the samelocus in another (e.g., normal, non-diseased) cell or a second locus.

By using at least one methylation-sensitive or methylation-dependentrestriction enzyme under conditions that allow for at least some copiesof potential restriction enzyme cleavage, sites in the locus to remainuncleaved and subsequently quantifying the remaining intact copies andcomparing the quantity to a control, average methylation density of alocus can be determined. If the methylation-sensitive restriction enzymeis contacted to copies of a DNA locus under conditions that allow for atleast some copies of potential restriction enzyme cleavage sites in thelocus to remain uncleaved, then the remaining intact DNA will bedirectly proportional to the methylation density, and thus may becompared to a control to determine the relative methylation density ofthe locus in the sample. Similarly, if a methylation-dependentrestriction enzyme is contacted to copies of a DNA locus underconditions that allow for at least some copies of potential restrictionenzyme cleavage sites in the locus to remain uncleaved, then theremaining intact DNA will be inversely proportional to the methylationdensity, and thus may be compared to a control to determine the relativemethylation density of the locus in the sample. Such assays aredisclosed in, e.g., U.S. Pat. No. 7,910,296.

Quantitative amplification methods (e.g., quantitative PCR orquantitative linear amplification) can be used to quantify the amount ofintact DNA within a locus flanked by amplification primers followingrestriction digestion. Methods of quantitative amplification aredisclosed in, e.g., U.S. Pat. No. 6,180,349; No. 6,033,854; and No.5,972,602, as well as in, e.g., DeGraves, et al., 34(1) BIOTECHNIQUES106-15 (2003); Deiman B, et al., 20(2) MOL. BIOTECHNOL. 163-79 (2002);and Gibson et al., 6 GENOME RESEARCH 995-1001 (1996). Amplifications maybe monitored in “real time.”

Additional methods for detecting DNA methylation can involve genomicsequencing before and after treatment of the DNA with bisulfite. See,e.g., Frommer et al., 89 PROC. NATL. ACAD. SCI. USA 1827-31 (1992). Whensodium bisulfite is contacted to DNA, unmethylated cytosine is convertedto uracil, while methylated cytosine is not modified. In someembodiments, restriction enzyme digestion of PCR products amplified frombisulfite-converted DNA is used to detect DNA methylation. See, e.g.,Xiong & Laird, 25 NUCLEIC ACIDS RES. 2532-34 (1997); and Sadri &Hornsby, 24 NUCL. ACIDS RES. 5058-59 (1996).

In some embodiments, a MethyLight assay is used alone or in combinationwith other methods to detect DNA methylation. See, Eads et al., 59CANCER RES. 2302-06 (1999). Briefly, in the MethyLight process genomicDNA is converted in a sodium bisulfite reaction (the bisulfite processconverts unmethylated cytosine residues to uracil). Amplification of aDNA sequence of interest is then performed using PCR primers thathybridize to CpG dinucleotides. By using primers that hybridize only tosequences resulting from bisulfite conversion of unmethylated DNA, (oralternatively to methylated sequences that are not converted)amplification can indicate methylation status of sequences where theprimers hybridize. Similarly, the amplification product can be detectedwith a probe that specifically binds to a sequence resulting frombisulfite treatment of a unmethylated (or methylated) DNA. If desired,both primers and probes can be used to detect methylation status. Thus,kits for use with MethyLight can include sodium bisulfite as well asprimers or detectably-labeled probes (including but not limited toTaqman or molecular beacon probes) that distinguish between methylatedand unmethylated DNA that have been treated with bisulfite. Other kitcomponents can include, e.g., reagents necessary for amplification ofDNA including but not limited to, PCR buffers, deoxynucleotides; and athermostable polymerase.

In other embodiments, a Methylation-sensitive Single Nucleotide PrimerExtension (Ms-SNuPE) reaction is used alone or in combination with othermethods to detect DNA methylation. See Gonzalgo & Jones, 25 NUCLEICACIDS RES. 2529-31 (1997). The Ms-SNuPE technique is a quantitativemethod for assessing methylation differences at specific CpG sites basedon bisulfite treatment of DNA, followed by single-nucleotide primerextension. Briefly, genomic DNA is reacted with sodium bisulfite toconvert unmethylated cytosine to uracil while leaving 5-methylcytosineunchanged. Amplification of the desired target sequence is thenperformed using PCR primers specific for bisulfite-converted DNA, andthe resulting product is isolated and used as a template for methylationanalysis at the CpG site(s) of interest. Typical reagents (e.g., asmight be found in a typical Ms-SNuPE-based kit) for Ms-SNuPE analysiscan include, but are not limited to: PCR primers for specific gene (ormethylation-altered DNA sequence or CpG island); optimized PCR buffersand deoxynucleotides; gel extraction kit; positive control primers;Ms-SNuPE primers for a specific gene; reaction buffer (for the Ms-SNuPEreaction); and detectably-labeled nucleotides. Additionally, bisulfiteconversion reagents may include: DNA denaturation buffer; sulfonationbuffer; DNA recovery regents or kit (e.g., precipitation,ultrafiltration, affinity column); desulfonation buffer; and DNArecovery components.

In further embodiments, a methylation-specific PCR reaction is usedalone or in combination with other methods to detect DNA methylation. Amethylation-specific PCR assay entails initial modification of DNA bysodium bisulfite, converting all unmethylated, but not methylated,cytosines to uracil, and subsequent amplification with primers specificfor methylated versus unmethylated DNA. See, Herman et al., 93 PROC.NATL. ACAD. SCI. USA 9821-26, (1996); and U.S. Pat. No. 5,786,146.

Additional methylation detection methods include, but are not limitedto, methylated CpG island amplification (see, Toyota et al., 59 CANCERRES. 2307-12 (1999)) and those methods described in, e.g., U.S. Pat. No.7,553,627; No. 6,331,393; U.S. patent Ser. No. 12/476,981; U.S. PatentPublication No. 2005/0069879; Rein. et al., 26(10) NUCLEIC ACIDS RES.2255-64 (1998); and Olek et al., 17(3) NAT. GENET. 275-6 (1997).

III. Determination of a Patient's Colorectal Cancer Status

The present invention relates to the use of biomarkers to detect CRC.More specifically, the biomarkers of the present invention can be usedin diagnostic tests to determine, qualify, and/or assess CRC status, forexample, to diagnose CRC, in an individual, subject or patient. Morespecifically, the biomarkers to be detected in diagnosing CRC include,but are not limited to, VSX2, NPTX1, BEND4, ALX3, miR34b, BTG4, GLP1R,HOMER2, GJC1, DOCK8, ZNF583, and NME4. Other biomarkers known in therelevant art may be used in combination with the biomarkers describedherein including, but not limited to, BMP3, GATA4, GATA5, H1C1, HPP1,ITGA4, MAL, MGMT, NDRG4, NELL1, OSMR, RASSF2, SFRP2, TFPI2, VIM, ANDWIF1.

A. Biomarker Panels

The biomarkers of the present invention can be used in diagnostic teststo assess, determine, and/or qualify (used interchangeably herein) CRCstatus in a patient. The phrase “CRC status” includes anydistinguishable manifestation of the disease, including non-disease. Forexample, CRC status includes, without limitation, the presence orabsence of CRC in a patient), the risk of developing CRC, the stage ofCRC, the progress of CRC (e.g., progress of CRC over time) and theeffectiveness or response to treatment of CRC (e.g., clinical follow upand surveillance of CRC after treatment). Based on this status, furtherprocedures may be indicated, including additional diagnostic tests ortherapeutic procedures or regimens.

The power of a diagnostic test to correctly predict status is commonlymeasured as the sensitivity of the assay, the specificity of the assayor the area under a receiver operated characteristic (“ROC”) curve.Sensitivity is the percentage of true positives that are predicted by atest to be positive, while specificity is the percentage of truenegatives that are predicted by a test to be negative. An ROC curveprovides the sensitivity of a test as a function of 1-specificity. Thegreater the area under the ROC curve, the more powerful the predictivevalue of the test. Other useful measures of the utility of a test arepositive predictive value and negative predictive value. Positivepredictive value is the percentage of people who test positive that areactually positive. Negative predictive value is the percentage of peoplewho test negative that are actually negative.

In particular embodiments, the biomarker panels of the present inventionmay show a statistical difference in different CRC statuses of at leastp<0.05, p<10⁻², p<10⁻³, p<10⁻⁴ or p<10⁻⁵. Diagnostic tests that usethese biomarkers may show an ROC of at least 0.6, at least about 0.7, atleast about 0.8, or at least about 0.9.

The biomarkers are differentially methylated in UI (or NC) and CRC, and,therefore, are useful in aiding in the determination of CRC status. Incertain embodiments, the biomarkers are measured in a patient sampleusing the methods described herein and compared, for example, topredefined biomarker levels and correlated to CRC status. In particularembodiments, the measurement(s) may then be compared with a relevantdiagnostic amount(s), cut-off(s), or multivariate model scores thatdistinguish a positive CRC status from a negative CRC status. Thediagnostic amount(s) represents a measured amount of a hypermethylatedbiomarker(s) above which or below which a patient is classified ashaving a particular CRC status. For example, if the biomarker(s) is/arehypermethylated compared to normal during CRC, then a measured amount(s)above the diagnostic cutoff(s) provides a diagnosis of CRC.Alternatively, if the biomarker(s) is/are hypomethylated in a patient,then a measured amount(s) at or below the diagnostic cutoff(s) providesa diagnosis of non-CRC. As is well understood in the art, by adjustingthe particular diagnostic cut-offs) used in an assay, one can increasesensitivity or specificity of the diagnostic assay depending on thepreference of the diagnostician. In particular embodiments, theparticular diagnostic cut-off can be determined, for example, bymeasuring the amount of biomarker hypermethylation in a statisticallysignificant number of samples from patients with the different CRCstatuses, and drawing the cut-off to suit the desired levels ofspecificity and sensitivity.

Indeed, as the skilled artisan will appreciate there are many ways touse the measurements of the methylation status of two or more biomarkersin order to improve the diagnostic question under investigation. In aquite simple, but nonetheless often effective approach, a positiveresult is assumed if a sample is hypermethylation positive for at leastone of the markers investigated.

Furthermore, in certain embodiments, the methylation values measured formarkers of a biomarker panel are mathematically combined and thecombined value is correlated to the underlying diagnostic question.Methylated biomarker values may be combined by any appropriate state ofthe art mathematical method. Well-known mathematical methods forcorrelating a marker combination to a disease status employ methods likediscriminant analysis (DA) (e.g., linear-, quadratic-, regularized-DA),Discriminant Functional Analysis (DFA), Kernel Methods (e.g., SVM),Multidimensional Scaling (MDS), Nonparametric Methods (e.g.,k-Nearest-Neighbor Classifiers), PLS (Partial Least Squares), Tree-BasedMethods (e.g., Logic Regression, CART, Random Forest Methods,Boosting/Bagging Methods), Generalized Linear Models (e.g., LogisticRegression), Principal Components based Methods (e.g., SIMCA),Generalized Additive Models, Fuzzy Logic based Methods, Neural Networksand Genetic Algorithms based Methods. The skilled artisan will have noproblem in selecting an appropriate method to evaluate a biomarkercombination of the present invention. In one embodiment, the method usedin a correlating methylation status of a biomarker combination of thepresent invention, e.g. to diagnose CRC, is selected from DA (e.g.,Linear-, Quadratic-, Regularized Discriminant Analysis), DFA, KernelMethods (e.g., SVM), MDS, Nonparametric Methods (e.g.,k-Nearest-Neighbor Classifiers), PLS (Partial Least Squares), Tree-BasedMethods (e.g., Logic Regression, CART, Random Forest Methods, BoostingMethods), or Generalized Linear Models (e.g., Logistic Regression), andPrincipal Components Analysis. Details relating to these statisticalmethods are found in the following references: Ruczinski et al., 12 J.OF COMPUTATIONAL AND GRAPHICAL STATISTICS 475-511 (2003); Friedman, J.H., 84 J. OF THE AMERICAN STATISTICAL ASSOCIATION 165-75 (1989); Hastie,Trevor, Tibshirani, Robert, Friedman, Jerome, The Elements ofStatistical Learning, Springer Series in Statistics (2001); Breiman, L.,Friedman, J. H., Olshen, R. A., Stone, C. J. Classification andregression trees, California: Wadsworth (1984); Breiman, L., 45 MACHINELEARNING 5-32 (2001); Pepe, M. S., The Statistical Evaluation of MedicalTests for Classification and Prediction, Oxford Statistical ScienceSeries, 28 (2003); and Duda, R. O., Hart, P. E., Stork, D. O., PatternClassification, Wiley Interscience, 2nd Edition (2001).

B. Determining Risk of Developing CRC

In a specific embodiment, the present invention provides methods fordetermining the risk of developing CRC in a patient. Biomarkermethylation percentages, amounts or patterns are characteristic ofvarious risk states, e.g., high, medium or low. The risk of developingCRC is determined by measuring the methylation status of the relevantbiomarkers and then either submitting them to a classification algorithmor comparing them with a reference amount, i.e., a predefined level orpattern of methylated (and/or unmethylated) biomarkers that isassociated with the particular risk level.

C. Determining CRC Severity

In another embodiment, the present invention provides methods fordetermining the severity of CRC in a patient. Each stage of CRC-stage 0,stage I, stage II, stage III, stage IV-has a characteristic level ofhypermethylation of a biomarker or relative hypermethylated levels of aset of biomarkers (a pattern). The severity of CRC is determined bymeasuring the methylation status of the relevant biomarkers and theneither submitting them to a classification algorithm or comparing themwith a reference amount, i.e., a predefined methylation level or patternof methylated biomarkers that is associated with the particular stage.

D. Determining CRC Prognosis

In one embodiment, the present invention provides methods fordetermining the course of CRC in a patient. CRC course refers to changesin CRC status over time, including CRC progression (worsening) and CRCregression (improvement). Over time, the amount or relative amount(e.g., the pattern) of hypermethylation of the biomarkers changes. Forexample, hypermethylation of biomarker “X” and “Y” may be increased withCRC. Therefore, the trend of these biomarkers, either increased ordecreased methylation over time toward CRC or non-CRC indicates thecourse of the disease. Accordingly, this method involves measuring themethylation level or status of one or more biomarkers in a patient atleast two different time points, e.g., a first time and a second time,and comparing the change, if any. The course of CRC is determined basedon these comparisons.

E. Patient Management

In certain embodiments of the methods of qualifying CRC status, themethods further comprise managing patient treatment based on the status.Such management includes the actions of the physician or cliniciansubsequent to determining CRC status. For example, if a physician makesa diagnosis of CRC, then a certain regime of monitoring would follow. Anassessment of the course of CRC using the methods of the presentinvention may then require a certain CRC therapy regimen. Alternatively,a diagnosis of non-CRC might be followed with further testing todetermine a specific disease that the patient might be suffering from.Also, further tests may be called for if the diagnostic test gives aninconclusive result on CRC status.

F. Determining Therapeutic Efficacy of Pharmaceutical Drug

In another embodiment, the present invention provides methods fordetermining the therapeutic efficacy of a pharmaceutical drug. Thesemethods are useful in performing clinical trials of the drug, as well asmonitoring the progress of a patient on the drug. Therapy or clinicaltrials involve administering the drug in a particular regimen. Theregimen may involve a single dose of the drug or multiple doses of thedrug over time. The doctor or clinical researcher monitors the effect ofthe drug on the patient or subject over the course of administration. Ifthe drug has a pharmacological impact on the condition, the amounts orrelative amounts (e.g., the pattern or profile) of hypermethylation ofone or more of the biomarkers of the present invention may change towarda non-CRC profile. Therefore, one can follow the course of themethylation status of one or more biomarkers in the patient during thecourse of treatment. Accordingly, this method involves measuringmethylation levels of one or more biomarkers in a patient receiving drugtherapy, and correlating the levels with the CRC status of the patient(e.g., by comparison to predefined methylation levels of the biomarkersthat correspond to different CRC statuses). One embodiment of thismethod involves determining the methylation levels of one or morebiomarkers at at least two different time points during a course of drugtherapy, e.g., a first time and a second time, and comparing the changein methylation levels of the biomarkers, if any. For example, themethylation levels of one or more biomarkers can be measured before andafter drug administration or at two different time points during drugadministration. The effect of therapy is determined based on thesecomparisons. If a treatment is effective, then the methylation status ofone or more biomarkers will trend toward normal, while if treatment isineffective, the methylation status of one or more biomarkers will trendtoward CRC indications.

G. Generation of Classification Algorithms for Qualifying CRC Status

In some embodiments, data that are generated using samples such as“known samples” can then be used to “train” a classification model. A“known sample” is a sample that has been pre-classified. The data thatare used to form the classification model can be referred to as a“training data set.” The training data set that is used to form theclassification model may comprise raw data or pre-processed data. Oncetrained, the classification model can recognize patterns in datagenerated using unknown samples. The classification model can then beused to classify the unknown samples into classes. This can be useful,for example, in predicting whether or not a particular biological sampleis associated with a certain biological condition (e.g., diseased versusnon-diseased).

Classification models can be formed using any suitable statisticalclassification or learning method that attempts to segregate bodies ofdata into classes based on objective parameters present in the data.Classification methods may be either supervised or unsupervised.Examples of supervised and unsupervised classification processes aredescribed in Jain, “Statistical Pattern Recognition: A Review”, IEEETransactions on Pattern Analysis and Machine Intelligence, Vol. 22, No.1, January 2000, the teachings of which are incorporated by reference.

In supervised classification, training data containing examples of knowncategories are presented to a learning mechanism, which learns one ormore sets of relationships that define each of the known classes. Newdata may then be applied to the learning mechanism, which thenclassifies the new data using the learned relationships. Examples ofsupervised classification processes include linear regression processes(e.g., multiple linear regression (MLR), partial least squares (PLS)regression and principal components regression (PCR)), binary decisiontrees (e.g., recursive partitioning processes such as CART), artificialneural networks such as back propagation networks, discriminant analyses(e.g., Bayesian classifier or Fischer analysis), logistic classifiers,and support vector classifiers (support vector machines).

Another supervised classification method is a recursive partitioningprocess. Recursive partitioning processes use recursive partitioningtrees to classify data derived from unknown samples. Further detailsabout recursive partitioning processes are provided in U.S. PatentApplication No. 2002 0138208 A1 to Paulse et al., “Method for analyzingmass spectra.”

In other embodiments, the classification models that are created can beformed using unsupervised learning methods. Unsupervised classificationattempts to learn classifications based on similarities in the trainingdata set, without pre-classifying the spectra from which the trainingdata set was derived. Unsupervised learning methods include clusteranalyses. A cluster analysis attempts to divide the data into “clusters”or groups that ideally should have members that are very similar to eachother, and very dissimilar to members of other clusters. Similarity isthen measured using some distance metric, which measures the distancebetween data items, and clusters together data items that are closer toeach other. Clustering techniques include the MacQueen's K-meansalgorithm and the Kohonen's Self-Organizing Map algorithm.

Learning algorithms asserted for use in classifying biologicalinformation are described, for example, in PCT International PublicationNo. WO 01/31580 (Barnhill et al., “Methods and devices for identifyingpatterns in biological systems and methods of use thereof”), U.S. PatentApplication Publication No. 2002/0193950 (Gavin et al. “Method oranalyzing mass spectra”), U.S. Patent Application Publication No.2003/0004402 (Hitt et al., “Process for discriminating betweenbiological states based on hidden patterns from biological data”), andU.S. Patent Application Publication No. 2003/0055615 (Zhang and Zhang,“Systems and methods for processing biological expression data”).

The classification models can be formed on and used on any suitabledigital computer. Suitable digital computers include micro, mini, orlarge computers using any standard or specialized operating System, suchas a Unix, Windows® or Linux™ based operating system. In embodimentsutilizing a mass spectrometer, the digital computer that is used may bephysically separate from the mass spectrometer that is used to createthe spectra of interest, or it may be coupled to the mass spectrometer.

The training data set and the classification models according toembodiments of the invention can be embodied by computer code that isexecuted or used by a digital computer. The computer code can be storedon any suitable computer readable media including optical or magneticdisks, sticks, tapes, etc., and can be written in any suitable computerprogramming language including R, C, C++, visual basic, etc.

The learning algorithms described above are useful both for developingclassification algorithms for the biomarker biomarkers alreadydiscovered, and for finding new biomarker biomarkers. The classificationalgorithms, in turn, form the base for diagnostic tests by providingdiagnostic values (e.g., cut-off points) for biomarkers used singly orin combination.

H. Kits for the Detection of CRC Biomarker Biomarkers

In another aspect, the present invention provides kits for qualifyingCRC status, which kits are used to detect or measure the methylationstatus/levels of the biomarkers described herein. Such kits can compriseat least one polynucleotide that hybridizes to at least one of thediagnostic biomarker sequences of the present invention and at least onereagent for detection of gene methylation. Reagents for detection ofmethylation include, e.g., sodium bisulfite, polynucleotides designed tohybridize to a sequence that is the product of a biomarker sequence ofthe invention if the biomarker sequence is not methylated (e.g.,containing at least one C→U conversion), and/or a methylation-sensitiveor methylation-dependent restriction enzyme. The kits can furtherprovide solid supports in the form of an assay apparatus that is adaptedto use in the assay. The kits may further comprise detectable labels,optionally linked to a polynucleotide, e.g., a probe, in the kit. Othermaterials useful in the performance of the assays can also be includedin the kits, including test tubes, transfer pipettes, and the like. Thekits can also include written instructions for the use of one or more ofthese reagents in any of the assays described herein.

In some embodiments, the kits of the invention comprise one or more(e.g., I, 2, 3, 4, or more) different polynucleotides (e.g., primersand/or probes) capable of specifically amplifying at least a portion ofa DNA region of a biomarker of the present invention including VSX2,NPTX1, BEND4, ALX3, miR34b, BTG4, GLP1R, HOMER2, GJC1, DOCK8, ZNF583,and NME4. Optionally, one or more detectably-labeled polypeptidescapable of hybridizing to the amplified portion can also be included inthe kit. In some embodiments, the kits comprise sufficient primers toamplify 2, 3, 4, 5, 6, 7, 8, 9, 10, or more different DNA regions orportions thereof, and optionally include detectably-labeledpolynucleotides capable of hybridizing to each amplified DNA region orportion thereof. The kits further can comprise a methylation-dependentor methylation sensitive restriction enzyme and/or sodium bisulfite.

In some embodiments, the kits comprise sodium bisulfite, primers andadapters (e.g., oligonucleotides that can be ligated or otherwise linkedto genomic fragments) for whole genome amplification, andpolynucleotides (e.g., detectably-labeled polynucleotides) to quantifythe presence of the converted methylated and or the convertedunmethylated sequence of at least one cytosine from a DNA region of abiomarker of the present invention including VSX2, NPTX1, BEND4, ALX3,miR34b, BTG4, GLP1R, HOMER2, GJC1, DOCK8, ZNF583, and NME4.

In some embodiments, the kits comprise methylation sensing restrictionenzymes (e.g., a methylation-dependent restriction enzyme and/or amethylation-sensitive restriction enzyme), primers and adapters forwhole genome amplification, and polynucleotides to, quantify the numberof copies of at least a portion of a DNA region of a biomarker of thepresent invention including VSX2, NPTX1, BEND4, ALX3, miR34b, BTG4,GLP1R, HOMER2, GJC1, DOCK8, ZNF583, and NME4.

In some embodiments, the kits comprise a methylation binding moiety andone or more polynucleotides to quantify the number of copies of at leasta portion of a DNA region of a biomarker of the present inventionincluding VSX2, NPTX1, BEND4, ALX3, miR34b, BTG4, GLP1R, HOMER2, GJC 1,DOCK8, ZNF583, and NME4. A methylation binding moiety refers to amolecule (e.g., a polypeptide) that specifically binds tomethyl-cytosine. Examples include restriction enzymes or fragmentsthereof that lack DNA cutting activity but retain the ability to bindmethylated DNA, antibodies that specifically bind to methylated DNA,etc.).

Without further elaboration, it is believed that one skilled in the art,using the preceding description, can utilize the present invention tothe fullest extent. The following examples are illustrative only, andnot limiting of the remainder of the disclosure in any way whatsoever.

EXAMPLES

The following examples are put forth so as to provide those of ordinaryskill in the art with a complete disclosure and description of how thecompounds, compositions, articles, devices, and/or methods described andclaimed herein are made and evaluated, and are intended to be purelyillustrative and are not intended to limit the scope of what theinventors regard as their invention. Efforts have been made to ensureaccuracy with respect to numbers (e.g., amounts, temperature, etc.) butsome errors and deviations should be accounted for herein. Unlessindicated otherwise, parts are parts by weight, temperature is indegrees Celsius or is at ambient temperature, and pressure is at or nearatmospheric. There are numerous variations and combinations of reactionconditions, e.g., component concentrations, desired solvents, solventmixtures, temperatures, pressures and other reaction ranges andconditions that can be used to optimize the product purity and yieldobtained from the described process. Only reasonable and routineexperimentation will be required to optimize such process conditions.

Materials and Methods

Patients and Nucleic Acid Preparation.

Sporadic CRC tissues were obtained during surgery. Adenomas wereobtained during colonoscopy. All adenomas were R1 cm in diameter orexhibited advanced histology (i.e., tubulovillous adenomas, villousadenomas, and adenomas with focal highgrade dysplasia). Recurrent CRCpatients, polyposis- or inflammatory bowel disease (CRC)-associated CRCpatients, and patients who had ever undergone chemotherapy for CRC orother neoplasias before sampling were excluded from the study.

Three types of non-neoplastic colonic mucosae (NCs) were studied: NCsfrom CRC patients (CRC-NCs), NCs from neoplasia-free subjects who were40 years of age or older (control NCs), and NCs from neoplasia-freesubjects who were younger than 40 years of age (young control NCs).Neoplasia-free subjects were those who underwent screening colonoscopybut presented no colonoscopic abnormalities and possessed no history ofcolonic neoplasia, CRC, or chemotherapy for any malignancies.

Tissue acquisition was conducted under a protocol approved by theinstitutional review board at the Johns Hopkins University (Baltimore,Md., USA). Written consent was obtained from all patients enrolled afterfull explanation of the purpose and nature of all procedures used.Genomic DNA was extracted from snap-frozen tissues using a DNeasy kit(Qiagen). Demographic data for cases studied inmicroarray/methylation-specific PCR (MSP) experiments and real-timequantitative MSP (qMSP) experiments are summarized in FIG. 4. Allspecimens interrogated in microarray experiments were also included inqMSP experiments. CpG island methylator phenotype (CIMP) status of eachtumor was determined based on qMSP measurement of the methylation statusof five loci (RUNX3, SOCS 1, NEUROG1, IGF2, and CACNA1G). Weisenbergeret al., 38 787-93 (2006). Neoplasias demonstrating methylation at ≧3 or<3 of the five loci were classified as CIMP-positive (+) or -negative(−) respectively.

Methylated CpG Island Amplification Coupled with Microarray Analysis.

Methylated CpG island amplification coupled with microarray (MCAM) wasconducted using the isoschizomers SmaI and XmaI. See Estecio et al., 17GENOME RES. 1529-36 (2007). 244K Human CGI microarrays (AgilentTechnologies, Santa Clara, Calif., USA) were employed as an arrayplatform. Using this methodology, the methylation status of 34 396SmaI-XmaI restriction fragments that covered to 50.4% of all CGIs in thegenome was assessed. Ssst-treated fully methylated DNA was used as acontrol DNA. Normalized log2 array intensity ratio to control fullymethylated DNA at each locus (log2 array ratio) was used to representlocus methylation level. The robustness of this MCAM methodology wasverified as follows: two separate MCAM experimental batches of aspecimen displayed markedly high reproducibility (R>0.99; FIG. 9A) andmethylation measurements by MCAM and qMSP were significantly correlated(R>0.70; FIG. 9B). Further methodological details are described in theSupplementary Materials and Methods section below.

Selection of Candidate Cancer-Specific Methylation Targets Based on theMCAM Data.

The criteria for autosomal cancer-specific methylation events in thecolon were as follows: 1) mean log2 array ratio for CRCs greater thanthat for control NCs by more than 0.5 at t-test P<0.01; 2) no overlap inlog2 array ratio between any CRCs versus any control NCs; 3) mean log2array ratio for CRCs greater than the lower 95% confidence limits ofmean normalized log2 array ratios for array normalization control probes(see Supplementary Materials and Methods below); and 4) mean log2 arrayratio for control NCs greater than the upper 95% confidence limits ofmean log2 array ratios for normalization control probes.

Methylation-Specific PCR.

MSP analyses were performed on pooled primary CRC-derived DNAs versuspooled control NC-derived DNAs. Specimens analyzed by MSP were identicalto those analyzed by MCAM. Thirty-seven cycles of PCR amplification werecarried out, and PCR product quantity was measured by gelelectrophoresis using a GelDoc XR system (Bio-Rad). Both the lack ofamplification from unmethylated control DNA and efficient amplificationfrom fully methylated control DNA were verified. A given locus wasclassified as hypermethylated in CRC when the visualized PCR productfrom pooled CRCs was greater than five-fold more abundant than frompooled control NCs. Primer sequences are shown in FIG. 6, seeSupplementary Materials and Methods below. Real-Time Quantitative MSP.qMSP was performed by the same primer set as for MSP and alocus-specific TaqMan probe for each locus. The fraction of denselymethylated DNA molecules at each locus (i.e., percent methylation ratio(PMR)) was calculated as described. See Mori et al., 131GASTROENTEROLOGY 797-808 (2006). TaqMan probe sequences are provided inFIG. 6.

Statistical Analysis.

A P value of 0.05 was used as the cut off for statistical significance.Normalized MCAM data were assessed by Student's t-tests, unlessotherwise stated. qMSP data were analyzed by Mann-Whitney U test, unlessotherwise stated, due to their non-normal distribution. Receiveroperator characteristic (ROC) curve analysis was applied to evaluate thediagnostic performance of PMR data at each locus. ROC curves weregenerated using the PMR data for each locus as a continuous inputvariable. The non-parametric Delong-Clarke-Pearson method was applied tocompare areas under ROC curves (AUROCs). See DeLong et al., 44BIOMETRICS 837-45 (1988). Forward stepwise discriminant analysis andfivefold cross validation were employed to generate diagnostic modelsbased on methylation levels at multiple loci.

Supplementary Methods and Materials

Methylated CpG Island Amplification (MCA).

Selective enrichment of methylated DNA in each sample DNA was conductedby utilizing the MCA methodology. In MCA, the methylated DNA-specificamplification was carried out based upon the serial digestion with a setof isoschizomers, methylation-sensitive SmaI and methylation-insensitiveXmaI, followed by XmaI-digested fragment-specific linker PCR. In brief,5 μg of DNA was digested with SmaI and then dephosphorylated usingAntarctic phosphatase. DNA was subsequently subjected to digestion withXmaI followed by column-purification with the QIAquick PCR purificationkit (Qiagen). The purified DNA was then ligated to linker by using T4DNA ligase and column-purified again. The linker was prepared byannealing the following two oligomers: RMCA24(5′-CCACCGCCATCCGAGCCTTTCTGC-3′) (SEQ ID NO:43) and RMCA12(5′-CCGGGCAGAAAG-3) (SEQ ID NO:44). One hundred ng of linker-ligated DNAwas PCR-amplified in a 100 μl reaction mix containing 100 μM of RMCA24as described previously. See Estecio et al., 17 GENOME RES. 1529-36(2007).

MCA Microarray.

The 244K Human CpG Island microarray (Agilent Technologies, Santa Clara,Calif.) was used as the array platform. The hybridization targets wereprepared by labeling 5 μg of MCA-processed DNA with Cy-5 or Cy-3 dUTPusing the random primer method (BioPrime DNA Labeling System,Invitrogen, Carlsbad, Calif.). Array hybridization and washing wascarried out according to the Agilent CGH microarray protocol. Array rawdata acquisition was conducted using an Agilent G2565BA microarrayscanner and Feature Extraction Software (Agilent) according to thearray-CGH data extraction protocol.

Array Data Processing.

Raw data processing included background subtraction and LOESSnormalization using the LIMMA scripts. See Wettenhall et al., 20BIOINFORMATICS 3705-06 (2004). LOESS normalization was performed basedon the probes whose corresponding SmaI-XmaI fragment length were greaterthan 5 kb and thus were not susceptible to PCR-amplification regardlessof the methylation status, as was described previously. See Estecio etal., 17 GENOME RES. 1529-36 (2007). The normalized log2 intensity ratioto the fully methylated control DNA, CpGenome Universal Methylated DNA(Millipore, Billerica, Mass.), at each locus was used as the valuerepresenting the locus methylation status. When multiple probescorresponded to a SmaI-XmaI fragment, the median of these probes wereused as the representative value. The data for probes whosecorresponding SmaI-XmaI fragments were 60-2000 nucleotides in length(i.e., optimal size for PCR amplification) were used for the evaluationof loci methylation status. Id. Fragments were dropped from analyseswhen only single corresponding probe presented on the array and the rawsignal intensity for this probe was low (i.e., <500 AU) in fullymethylated control DNA. Id. As the results, this MCAM protocol enabledthe methylation status assessment of 34,396 SmaI-XmaI restrictionfragments that corresponded to 14,213 CGIs (50.4% of all CGIs in thegenome). Twenty percent of these SmaI-XmaI fragments located outside ofknown coding or non-coding genes, and the remaining fragments locatedproximal to the transcriptional start sites (−2,000 to +500 bases, 63%)or transcribed regions (17%). Annotation of the probes and SmaI-XmaIfragments was based upon the Human Genome Assembly version 18. Array rawdata processing was conducted using a LIMMA library-based R script andan sql script.

Results

A genome-wide search was conducted for novel targets of CRC-specifichypermethylation by employing methylated DNA microarray-based scanningof primary CRCs followed by locus-specific qMSPbased validation. SeeFIG: 1. A total of 33,414 autosomal CGI loci were interrogated. Afterperforming qualitative validation in the tissue cohort that was used inthe microarray analysis, quantitative validation was carried out in alarger tissue cohort utilizing locus-specific qMSP-based assays.

Example 1 Microarray Screening

Methylated DNA microarray analysis was performed by MCAM methodology.See Estecio et al., 17 GENOME RES. 1529-36 (2007). Seventeen primaryCRCs and eight non-neoplastic colonic mucosae (NCs) from colonicneoplasia-free control subjects who were 40 years of age or older(control NCs) were analyzed (FIG. 6). Aged control individuals werestudied to avoid mistakenly identifying age-associated hypermethylationtargets as neoplasiaspecific hypermethylation events. Matchingnonneoplastic colonic tissues from CRC cases (hereinafter referred to asNC-CRC) were not used as controls, since these tissues may already carryhypermethylation events linked to an increased risk of carcinogenicprogression due to a “field defect.” See Belshaw et al., 31CARCINOGENESIS 1158-63 (2010), Svrcek et al., 59 GUT 1516-26 (2010),Nosho et al., 137 GASTROENTEROLOGY 1609-20 (2009), and Shen et al., 97J. NAT. CANCER INST. 1330-38 (2005).

The majority of analyzed loci tended to be differentially methylated inCRCs relative to control NCs (P>0.1: 18,892 of 33,414 analyzed autosomalloci). Cluster analyses of these 18,892 loci showed separation of CRCsfrom control NCs (FIG. 10, see Supplementary Materials and Methodsabove). As expected, CIMP (+) and CIMP (−) CRCs clustered separately,with the exception of one CIMP (−) CRC that was methylated at two CIMPmarker loci and clustered with CIMP (+) CRCs. Candidate autosomal lociwere selected for colonic neoplasia-specific methylation based onsignificant hypermethylation in CRCs relative to control NCs by a meanlog2 array intensity ratio difference ≧0.5. To eliminate markers thatwould likely to exhibit low sensitivity and specificity in CRCdiagnosis, loci whose methylation level overlapped between CRCs andcontrol NCs were excluded (i.e., loci showing hypermethylation in CRC atwhich minimum log2 array ratio for CRCs is smaller than maximum log2array ratio for control NCs, and vice versa). Based on these criteria,169 loci were designated as candidate loci showing neoplasiaspecifichypermethylation in colonic mucosae.

One of these 169 loci was SFRP2, a previously published target ofcancer-specific methylation in the colon, whose methylation has beenreported in 75-90% of stool DNAs from CRC patients by multiple groups.See Nagasaka et al., 101 J. NAT. CANCER INST. 1244-58 (2009), Wang etal., 14 GASTROENTEROLOGY 524-31 (2008), Huang et al., 52 DIGESTIVE DIS.& SCI. 2287-91 (2007), and Muller et al., 363 LANCET 1283-85 (2004). Thecurrent MCAM study also confirmed significant hypermethylation ofseveral other previously reported CRC methylation markers in CRCsrelative to control NCs (such as RASSF2 and vimentin; FIG. 7, seeSupplementary Materials and Methods above). However, unlike SFRP2, theseloci demonstrated overlap in methylation levels between CRCs and controlNCs in our study, and were therefore not included among theaforementioned 169 loci.

Example 2 Individual Qualitative Validation of Prioritized Targets in aPilot Pooled Cohort

Twenty of the 169 candidate CRC-specific methylation target loci wereprioritized for further individual validation based on having shown thelargest differences between CRCs and control NCs and the smallestintra-group variance in array-based methylation levels (FIG. 8, seeSupplementary Materials and Methods above). These 20 loci were thenanalyzed by qualitative MSP, using pooled DNA specimens for CRCs andcontrol NCs that had been studied in microarray scanning experiments.Specimens were pooled to avoid exhaustion of limited clinical DNAresources. It was reasoned that the previous and subsequent non-pooledanalyses (i.e., microarray and qMSP assays) would eliminatefalse-positive findings caused by sample pooling (e.g., massivehypermethylation occurring in only a minority of CRCs). Hypermethylationin pooled CRCs versus pooled control NCs was observed at 16 of the 20analyzed loci: SFRP2, visual system homeobox 2 (VSX2), BEN domaincontaining 4 (BEND4), ALX homeobox 3 (ALX3), neuronal pentraxin I(NPTX1), glucagon-like peptide 1 receptor (GLP1R), homer homolog 2(HOMER2), gap junction protein, gamma 1 (GJC1), dedicator ofbiomarkersis 8 (DOCK8), nonmetastatic cells 4 (NME4), zinc fingerprotein 583 (ZNF583), transmembrane protein 42 (TMEM42), tubulintyrosine ligase-like family, member 12 (TTLL12), miR-34b, and MDFI (FIG.8). The miR-34b locus flanks the region, that is, proximal to the BTG4gene transcriptional start site and is hypermethylated in ˜90% ofprimary CRCs. See Toyota et al., 68 CANCER RES. 4123-32 (2008).

Example 2 Quantitative Methylation Assays of Validated Targets in aLarger Cohort

Methylation of the qualitatively validated CRC-specific methylationtargets was then assessed in a larger cohort using a quantitativemethodology, qMSP. Two loci were eliminated before performing qMSP:MDFI, for failure to establish a successful qMSP assay and SFRP2, forhaving already been established as a CRC detection marker. See Nagasakaet al., 101 J. NAT. CANCER INST. 1244-58 (2009), Wang et al., 14GASTROENTEROLOGY 524-31 (2008), Huang et al., 52 DIGESTIVE DIS. & SCI.2287-91 (2007), and Muller et al., 363 LANCET 1283-85 (2004). The 14qMSP-tested loci comprised VSX2, BEND4, ALX3, NPTX1, GLP1R, HOMER2,GJC1, DOCK8, NME4, ZNF583, TMEM42, TTLL12, miR-34b, and BTG4 (i.e., thepreviously analyzed miR-34b-flanking region). The analyzed case-controlcohort contained 113 specimens: 51 primary CRCs, nine adenomas, 26control NCs from non-neoplasia patients, 19 NCs from CRC patients(CRC-NCs), and nine NCs from colon neoplasia-free cases who were youngerthan 40 years of age (young control NCs). The control NCs were analyzedas a base control group representing the target population foraverage-risk CRC screening. Case demographic data are given in FIG. 4.There were no significant differences in case age, a well-establishednon-neoplastic methylation promoting factor, between any groups exceptfor the young control NCs.

All 14 tested loci demonstrated varying degrees of hypermethylation inCRCs by qMSP assays. Significant hypermethylation in CRCs relative tocontrol NCs was observed at all tested loci except DOCK8, NME4, TMEM42,and TTLL12 (FIG. 2). These four loci demonstrated tumor-specificmethylation in a minor subset of CRCs. NME4, TMEM42, and TTLL12 weremethylated in <10% of the 51 CRCs, and methylation of these loci wasobserved only in CRCs that had been studied by MCAM. Thus, these threeloci were eliminated from further analyses, leaving 11 loci for furtherstudy. No significant differences in methylation levels according to thegender, Dukes stage (AB versus CD), or microsatellite instability (MSI)status were observed at any of these 11 loci (data not shown). GJC1 wassignificantly more heavily methylated in proximal CRCs (median percentmethylation, or PMR, 10.8%) than in distal CRCs (0.8%; PZ0.02). CIMP (C)CRCs demonstrated significantly higher PMR levels than did CIMP (K) CRCsat ALX3, NPTX1, BTG4, GLP1R, HOMER2, DOCK8, and GJC1, although themajority of CIMP (K) CRCs were hypermethylated at all of these lociexcept DOCK8 (data not shown). DOCK8 was methylated in only 11 (25.6%)of 43 CIMP (K) CRCs, in contrast to CIMP (C) CRCs (four of five, or 80%;Fisher's exact test, PZ0.03).

Significant hypermethylation in adenomas relative to control NCs wasobserved at BEND4, VSX2, NPTX1, miR-34b, and HOMER2 (FIG. 2). OnlymiR-34b was methylated at equal levels in CRCs and adenomas (median PMR10.9 vs. 11.4% for CRCs versus adenomas respectively; P=0.76). Remainingfour loci were methylated at lesser degrees in adenomas than in CRCs,but these differences were insignificant. Tumor demographic dataanalyses were not performed for adenomas.

Notably, ALX3 was mildly but significantly hypermethylated in CRC-NCsrelative to control NCs (median PMR 1.6 vs. 0.6% for NC-CRCs versuscontrol NCs respectively; P=0.001; FIG. 2 and FIG. 11, see SupplementaryMaterials and Methods above). ALX3 methylation in CRC-NCs showed nosignificant association with age or corresponding CRC stage (data notshown). Methylation levels of NC from all CRC-free cases (viz., controlNCs and young control NCs) at BEND4, GJC1, VSX2, and miR-34b weresignificantly correlated JO with age (Spearman rank correlation R=0.55,0.51, 0.39, and 0.38 respectively; P<0.05). However, differences betweenolder and younger control NCs were small: median PMRs for old versusyoung NCs were 0.3 vs. 0.0%, 0.1 vs. 0.0%, 0.3 vs. 0.0%, and 1.4 vs.0.6%, for BEND4, GJC1, VSX2, and miR-34b respectively. These differenceswere smaller than those reported for classic age-dependenthypermethylation targets (e.g., N33 and estrogen receptor a (ESR1); seeAhuja et al., 58 CANCER RES. 5489-94 (1998), and Issa et al., 7 NAT.GEN. 536-40 (1994)). Association between gender and gene methylation wasnot assessed due to the small number of female control NC cases studied(n=2).

Example 3 Evaluation of Methylated Loci as Colonic Neoplasia Markers

The 11 CRC-specific methylation targets were next tested for theirabilities to distinguish colonic neoplasias from control NCs byemploying ROC curve analysis. Methylation levels at all locisignificantly distinguished CRCs from control NCs (P<0.05; FIG. 5). VSX2achieved the highest discriminative accuracy (AUROC, 92.3, 83.3%sensitivity and 92.3% specificity; FIG. 3A). BEND4, ALX3, NPTX1,miR-34b, BTG4, and GLP1R also achieved particularly high diagnosticaccuracy (AUROC>0.8, P<1×10⁻⁶; FIG. 3A). There was no statisticallysignificant difference in AUROC between discrimination of Dukes ABversus Dukes CD CRCs from control NCs for all but one locus: ALX3discriminated Dukes AB CRCs significantly better than Dukes CD CRCs(P<0.03; FIG. 5). Five loci significantly distinguished adenomas fromcontrol NCs: VSX2, BEND4, NPTX 1, miR-34b, and HOMER2 (P<0.05; FIG. 5),despite our relatively small adenoma cohort size (n=9). BTG4 alsodemonstrated weak discriminative capacity in this regard (P=0.09). ALX3was capable of significantly distinguishing CRC-NCs from control NCs(P=5.1×10⁻⁵; FIG. 5). ZNF583 and BEND4 exerted similar significantdiscriminative abilities (P<0.05), but the lower 95% confidence limitfor their AUROCs did not exceed 0.5. Age did not significantlydiscriminate any diseased tissue classes from control NCs, as expectedfrom the age-matched study enrollment strategy (data not shown). The useof a multilocus methylation panel improved the discrimination of CRC-NCsfrom control. NCs (AUROC 0.83; 95% CI 0.69-0.92) relative to thebest-performing single locus (ALX3), although this improvement wasinsignificant (FIG. 3B). The loci included in this multilocus panel wereALX3, ZNF583, miR-34b, and VSX2. The use of multilocus methylationpanels did not improve the discrimination of CRCs from NCs relative tothe best-performing single locus (VSX2; data not shown).

Example 4 Evaluation of Methylation Biomarkers in Stool Specimens

To determine whether methylation biomarkers identified in primary tumorsare also useful in fecal DNA-based diagnosis, feces was collected andDNA was analyzed from 54 cases comprising 27 colorectal cancer (CRC), 21colorectal adenoma (CRA) and 6 non-neoplastic control subjects (NC).Using quantitative bisulfate pyrosequencing, the performance of thenovel methylation biomarker ALX3 was evaluated in stool DNA from CRC andCRA patients vs. NCs. As shown in FIG. 12A, stool methylation level wassignificantly higher in CRA and CRC patients than in healthy controls(t-test p<0.05 for both comparisons). The performance of this marker indiagnosing patients with advanced colorectal neoplasis (i.e., both CRAand CRC) is extremely high (AUROC, 0.94; FIG. 12B). Overall, theseresults reinforce the robustness and capability of the present approachto identify reliable and accurate methylation biomarkers for the earlydetection of colorectal neoplasia.

Example 5 Evaluation of Serum Methylation Biomarkers

The performance of two methylation biomarkers, ALX3 and miR-34b,identified in a preliminary cohort of plasma samples from CRC patients(n=9) and healthy subject (n=10) was analyzed. DNA was extracted from 1ml of serum, bisulfite conversion was performed, and methylation wasanalyzed using MethyLight that detects densely methylated DNA moleculesin a sequence-specific fashion. Methylated DNA molecules were detectedat both ALX3 and miR-34b in a subset of CRC patients' serum (FIG. 13).In contrast, no methylation was detected at ALX3 or miR-34b in any ofthe healthy subjects' serum. This experiment establishes the feasibilityof detecting methylated alleles of ALX and miR-34b as plasma-basedpotential biomarkers for diagnosing CRC patients. Due to the smallnumber of the cohort size, AUROC was no assessed.

Discussion

This unbiased genome-wide methylomics scan identified 169 candidatehypermethylation targets in human primary CRCs. The validity of themethod was supported by finding significant hypermethylation ofpreviously reported genes undergoing hypermethylation in CRC, includingSFRP2. See Nagasaka et al., 101 J. NAT. CANCER INST. 1244-58 (2009);Huang et al., 52 DIGESTIVE DIS. & SCI. 2287-91 (2007); and Muller etal., 363 LANCET 1283-85 (2004). Individual qMSP assessment ofsystematically prioritized loci validated frequent hypermethylation inprimary CRCs at 11 loci: VSX2, NPTX1, BEND4, ALX3, miR-34b, BTG4, GLP1R,HOMER2, GJC1, DOCK8, and ZNF583. Infrequent but neoplasiaspecificmethylation was observed at three additional loci: NME4, TTLL12, andTMEM42. Hypermethylation at each of these 11 loci effectivelydiscriminated CRCs from colonic mucosae of age-matched neoplasia-freecases (i.e., control NCs). Most of these loci exhibited highdiscriminative accuracy (i.e. AUROC>0.8 and P<1×10⁻⁶), with VSX2performing the best (AUROC=0.93). Methylation levels of VSX2, NPTX1,BEND4, miR-34b, and HOMER2 also significantly differentiated adenomasfrom control NCs (AUROC 0.74-0.83) and may constitute ideal markers forearly-stage disease detection and/or risk stratification. The observedAUROC values for CRC and adenoma discrimination were very high evenunder the current study conditions (i.e., use of age-matched controlcases and lack of tumor cell enrichment by microdissection, both ofwhich reduce methylation-based discriminative accuracy).

It is also notable that CRC cases, regardless of their CIMP status, weredistinguished from age-matched neoplasia-free cases based onhypermethylation of normeoplastic colonic mucosae at certain loci (suchas ALX3). This finding is reminiscent of recent reports showing thatCRC-associated hypermethylation target loci are mildly hypermethylatedin non-neoplastic colonic mucosae from colonic neoplasia patients. SeeWorthley et al., 29 ONCOGENE 1653-62 (2010); Ahlquist et al., 7 MOL.CANCER 94 (2008); Belshaw et al., 99 BR. J. CANCER 136-42 (2008);Menigatti et al., 17 ONCOLOGY REPORTS 1421-27 (2007). However, in thesepublished reports, differential methylation of normeoplastic mucosae wasCIMP (+) neoplasia case-specific, or based on data from non-age-matchedsubjects. The present findings in non-neoplastic mucosae support thenotion that CRC-associated hypermethylation initiates at an early,non-neoplastic stage, representing a widespread ‘field defect.’ SeeBelshaw et al., 31 CARCINOGENESIS 1158-63 (2010); Svrcek et al., 59 GUT1516-26 (2010); Nosho et al., 137 GASTROENTEROLOGY 1609-20 (2009); andShen et al., 97 J. NAT. CANCER INST. 1330-38 (2005). Thesenon-neoplastic mucosal methylation events should be clinicallytranslatable into CRC risk prediction, by non-neoplastic colonic orrectal mucosa as an analytic substrate. Moreover, CRC detection markerswhose CRC-associated hypermethylation initiates at non-neoplastic stagemay perform better in stool DNA-based tests than in primary tissueDNAbased tests, since stool DNA is derived from both nonneoplastic andneoplastic colonic mucosal cells.

The current MCAM study also detected CRC-associated hypermethylation ofmultiple previously published CRC-specific methylation markers,including the most extensively studied methylation marker to date,vimentin. See Li et al., 27 NAT. BIOTECH. 858-63 (2009); Baek et al., 52DIS. COLON AND RECTUM 1452-63 (2009); Ahlquist et al., 149 ANN. INTERNALMED. 441-50 (2008); Itzkowitz et al., 5 CLIN. GASTROENTEROLOGY &HEPATOLOGY 111-17 (2007); Chen et al., 97 J. NAT. CANCER INST. 1124-32(2005). However, these markers, except for SFRP2, demonstratedmethylation overlap between CRCs and NCs in the MCAM tissue cohort, andthus did not satisfy the selection criteria. Estecio et al. (2007) alsoperformed MCAM on CRCs mainly focusing on CIMP class-based profiling,and reported hypermethylation of BARHL1 and RSH1. Estecio et al., 17GENOME RES. 1529-36 (2007). The present MCAM study verified significantCRC-associated hypermethylation of BARHL1, but not of RSHL1. Selectioncriteria was designed to eliminate CRC-associated hypermethylationtargets that were also moderately methylated in nonneoplastic colonicmucosae of neoplasia-free cases, since they would not be anticipated toperform well as stool biomarkers, due to normal DNA contamination instool DNA. As proof-of-principle of the success of the strategy, thecurrent candidates did not include previously reported targetsexhibiting this type of methylation (e.g., SST and CAV1, which werepreviously identified, Mod et al., 131 GASTROENTEROLOGY 797-808 (2006)).

The present study represents the first report of neoplasia-associatedhypermethylation of VSX2, BEND4, GL1R, HOMER2, GJC1, ZNF583, and NME4 inany tumor type. The loci detected in this study should be explored foruse as broad-spectrum malignancy biomarkers, especially in blood-baseddetection studies.

In summary, this study has successfully applied an unbiased, extensivegenome-wide scanning strategy to discover neoplasia-specific methylationtargets in the colon, identifying 169 candidate novel loci. QuantitativePCR-based analysis of prioritized loci in a larger patient cohortrevealed that methylation events at 11 loci were accurate indistinguishing both neoplastic and non-neoplastic colonic mucosae ofcolonic neoplasia patients from control colonic mucosae ofneoplasia-free patients. Two of these genes have been implicated inendocrine-related carcinogenesis. Methylation at these loci now meritsfurther investigation in studies of independent cohort validation,stool- and plasma-based CRC detection, as well as in the evaluation ofnon-neoplastic mucosa for field defects, potentially indicatingincreased CRC susceptibility.

REFERENCES

-   1. Ahlquist et al., 149 ANN. INTERNAL MED. 441-50 (2008)-   2. Ahlquist et al., 7 MOL. CANCER 94 (2008)-   3. Ahuja et al., 58 CANCER RES. 5489-94 (1998)-   4. Ausch et al., 55 CLIN. CHEM. 1559-63 (2009)-   5. Ayala et al., 151 ENDOCRINOLOGY 4678-87 (2010)-   6. Baek et al., 52 DIS. COLON AND RECTUM 1452-63 (2009)-   7. Belshaw et al., 13 CANCER EPIDEMIOL. BIOMARKERS PREV. 1495-1501    (2004)-   8. Belshaw et al., 99 BR. J. CANCER 136-42 (2008)-   9. Belshaw et al., 31 CARCINOGENESIS 1158-63 (2010)-   10. Campos et al., 134 ENDOCRINOLOGY 2156-64 (1994)-   11. Chen et al., 97 J. NAT. CANCER INST. 1124-32 (2005)-   12. DeLong et al., 44 BIOMETRICS 837-45 (1988)-   13. Dong et al., 387 BIOCHEM. & BIOPHYS. RES. COMM. 132-38 (2009)-   14. Eads et al., 28 NUCL. ACIDS RES. E32 (2000)-   15. Ebert et al., 131. GASTROENTEROLOGY 1418-30 (2006)-   16. Estecio et al., 17 GENOME RES. 1529-36 (2007)-   17. Fraga et al., 23 TRENDS IN GEN. 413-18 (2007)-   18. Glockner et al., 69 CANCER RES. 4691-99 (2009)-   19. Gomes et al., 84 BIOL. REPRODUCTION 52-61 (2011)-   20. Hadjiyanni et al., 53 DIABETOLOGIA 730-40 (2010)-   21. Hagihara et al., 23 ONCOGENE 8705-10 (2004)-   22. Hellebrekers et al., 15 CLIN. CANCER RES. 3990-97 (2009)-   23. Hiyama et al., 34 EXP. LUNG RES. 373-90 (2008)-   24. Hogan et al., 307 MOL. CELL. ENDOCRINOLOGY 19-24 (2009)-   25. Huang et al., 52 DIGESTIVE DIS. & SCI. 2287-91 (2007)-   26. Issa et al., 7 NAT. GEN. 536-40 (1994)-   27. Itzkowitz et al., 287 AM. J. PHYSIOL., GASTROINTESTINAL & LIVER    PHYSIOL. G7-17 (2004)-   28. Itzkowitz et al., 11 INFLAMMATORY BOWEL DISEASES 314-21 (2005)-   29. Itzkowitz et al., 5 CLIN. GASTROENTEROLOGY & HEPATOLOGY 111-17    (2007)-   30. Jemal et al., 58 CANCER J. FOR CLINICIANS 71-96 (2008)-   31. Kahi et al., 135 GASTROENTEROLOGY 380-99 (2008)-   32. Kim et al., 4 PLOS ONE e6555 (2009)-   33. Kim et al., 45 GENES, CHROMOSOMES & CANCER 781-89 (2006)-   34. Kozaki et al., 68 CANCER RES. 2094-2105 (2008)-   35. Larsson et al., 27 NAT. BIOTECH. 1679-87 (2005)-   36. Lee et al., 15 CLIN. CANCER RES. 6185-91 (2009)-   37. Lenhard et al., 3 CLIN. GASTROENTEROL. EIEPATOL. 142-49 (2005)-   38. Li et al., 27 NAT. BIOTECH. 858-63 (2009)-   39. Lin et al., INT. J. CANCER (2011) (in press)-   40. Lujambio et al., 105 PROC. NATL. ACAD. SCI. U.S.A. 13556-61    (2008)-   41. Maher et al., 250 ANN. SURGERY 729-37 (2009)-   42. Melotte et al., 101 J. NATL. CANCER INST. 916-27 (2009)-   43. Menigatti et al., 17 ONCOLOGY REPORTS 1421-27 (2007)-   44. Mori et al., 131 GASTROENTEROLOGY 797-808 (2006)-   45. Muller et al., 363 LANCET 1283-85 (2004)-   46. Nagasaka et al., 101 J. NAT. CANCER INST. 1244-58 (2009)-   47. Nosho et al., 137 GASTROENTEROLOGY 1609-20 (2009)-   48. Ogawa et al., 21 DISEASES OF THE ESOPHAGUS 288-97 (2008)-   49. Ongenaert et al., 1 BMC MEDICAL GENOMICS 57 (2008)-   50. Shen et al., 97 J. NAT. CANCER INST. 1330-38 (2005)-   51. Svrcek et al., 59 GUT 1516-26 (2010)-   52. Takahashi et al., 28 INT. J. ONCOLOGY 321-28 (2006)-   53. Tanzer et al., 5 PLOS ONE e9061 (2010)-   54. Toyota et al., 68 CANCER RES. 4123-32 (2008)-   55. Uhlmann et al., 23 ELECTROPHORESIS 4072-79 (2002)-   56. Wang et al., 14 GASTROENTEROLOGY 524-31 (2008)-   57. Weisenberger et al., 38 787-93 (2006)-   58. Wettenhall et al., 20 BIOINFORMATICS 3705-06 (2004)-   59. Wimmer et al., 33 GENES, CHROMOSOMES & CANCER 285-94 (2002)-   60. Worthley et al., 29 ONCOGENE 1653-62 (2010)-   61. Xu et al., 56 DIABETES 1551-58 (2007)-   62. Yang et al., 18 BIOMARKERS & PREV. 3000-07 (2009)-   63. Yasuhara et al., 79 BIOL. REPRODUCTION 432-41 (2008)-   64. Zou et al., 16 CANCER EPIDEMIOL. BIOMARKERS PREV. 2686-96 (2007)

1. A method for diagnosing colorectal cancer (CRC) in a patientcomprising the steps of: a. collecting a sample from the patient; b.measuring the methylation levels of one or more biomarkers in the samplecollected from the patient; and c. comparing the methylation levels ofthe one or more biomarkers with predefined methylation levels of thesame biomarkers that correlate to a patient having CRC and predefinedmethylation levels of the same biomarkers that correlate to a patientnot having CRC, wherein a correlation to one of the predefinedmethylation levels provides the diagnosis.
 2. The method of claim 1,wherein the one or more biomarkers is selected from the group consistingof VSX2, NPTX1, BEND4, ALX3, miR34b, BTG4, GLP1R, HOMER2, GJC1, DOCK8,ZNF583, and NME4.
 3. The method of claim 1, wherein the one or morebiomarkers comprise ALX3, miR34b or both.
 4. The method of claim 1,wherein the one or more biomarkers comprises VSX2.
 5. The method ofclaim 1, wherein the one or more biomarkers comprise VSX2, NPTX1, BEND4,miR34b, and HOMER2.
 6. The method of claim 1, wherein the one or morebiomarkers comprise VSX2, BEND4, GLP1R, HOMER2, GJC1, ZNF583.
 7. Themethod of claim 6, wherein the one or more biomarkers further comprisesNME4.
 8. The method of claim 1, wherein the sample is a stool, blood orserum sample.
 9. The method of claim 8, wherein the sample is a stoolsample.
 10. The method of claim 8, wherein the sample is a serum sample.11. A method for diagnosing colorectal cancer (CRC) in a patientcomprising the steps of: a. collecting a sample from the patient; b.measuring the methylation levels of a panel of biomarkers in the samplecollected from the patient, wherein the panel of biomarkers comprisesVSX2, NPTX1, BEND4, ALX3, miR34b, BTG4, GLP1R, HOMER2, GJC1, DOCKS,ZNF583, and NME4; and c. comparing the methylation levels of the panelof biomarkers with predefined methylation levels of the same panel ofbiomarkers that correlate to a patient having CRC and predefinedmethylation levels of the same panel of biomarkers that correlate to apatient not having CRC, wherein a correlation to one of the predefinedmethylation levels provides the diagnosis.
 12. A method for diagnosingcolorectal cancer (CRC) in a patient comprising the steps of: a.collecting a sample from the patient; b. measuring the methylationlevels of a panel of biomarkers in the sample collected from thepatient, wherein the panel of biomarkers comprises VSX2 and ALX3; and c.comparing the methylation levels of the panel of biomarkers withpredefined methylation levels of the same biomarkers that correlate to apatient having CRC and predefined methylation levels of the samebiomarkers that correlate to a patient not having CRC, wherein acorrelation to one of the predefined methylation levels provides thediagnosis.
 13. The method of claim 12, wherein the panel of biomarkersfurther comprises miR34b.
 14. The method of claim 12, wherein the panelof biomarkers further comprises miR34b, NPTX1, BEND4, BTG4, GLP1R,HOMER2, GJC1, DOCK8, ZNF583, and NME4.
 15. The method of claim 11,wherein the sample is a stool, blood or serum sample.
 16. The method ofclaim 15, wherein the sample is a stool sample.
 17. The method of claim15, wherein the sample is a serum sample.
 18. A method for diagnosingcolorectal cancer (CRC) in a patient comprising the steps of: a.collecting a stool sample from the patient; b. measuring the methylationlevels of a panel of biomarkers in the stool sample collected from thepatient, wherein the panel of biomarkers comprises ALX3 and miR34b; andc. comparing the methylation levels of the panel of biomarkers withpredefined methylation levels of the same biomarkers that correlate to apatient having CRC and predefined methylation levels of the samebiomarkers that correlate to a patient not having CRC, wherein acorrelation to one of the predefined methylation levels provides thediagnosis.
 19. The method of claim 18, wherein the panel of biomarkersfurther comprises VSX2.
 20. The method of claim 18, wherein the panel ofbiomarkers further comprises VSX2, NPTX1, BEND4, BTG4, GLP1R, HOMER2,GJC1, DOCK8, ZNF583, and NME4.
 21. A method for determining the CRCstatus in a patient comprising the steps of: a. collecting a sample fromthe patient; b. measuring the methylation levels of a panel ofbiomarkers in the sample collected from the patient, wherein the panelof biomarkers comprises VSX2, NPTX1, BEND4, ALX3, miR34b, BTG4, GLP1R,HOMER2, GJC1, DOCK8, ZNF583, and NME4; and c. comparing the methylationlevels of the panel of biomarkers with predefined methylation levels ofthe same panel of biomarkers that correlate to one or more CRC statusesselected from the group consisting of having CRC, not having CRC,progressing CRC, and regressing CRC, wherein a correlation to one of thepredefined methylation levels determines the CRC status of the patient.22. The method of claim 1, wherein the measuring step comprisesrestriction enzyme digestion of the sample followed by real-timequantitative methylation-specific polymerase chain reaction.
 23. Adiagnostic kit for determining CRC status in a patient comprising: a. asubstrate for collecting a biological sample from the patient; and b.means for measuring the methylation levels of one or more biomarkersselected from the group consisting of VSX2, NPTX1, BEND4, ALX3, miR34b,BTG4, GLP1R, HOMER2, GJC1, DOCK8, ZNF583, and NME4; and
 24. The kit ofclaim 23, wherein the means for measuring the methylation levels of oneor more biomarkers are oligonucleotide primers specific for amplifyingmethylated regions of the biomarkers.